This paper presents UDRST, an unlabeled discourse parsing system in the RST framework. UDRST consists of a segmentation model and a parsing model. The segmentation model exploits subtree features to rerank N-best outputs of a base segmenter, which uses syntactic and lexical features in a CRF framework. In the parsing model, we present two algorithms for building a discourse tree from a segmented text: an incremental algorithm and a dual decomposition algorithm. Our system achieves 77.3% in the unlabeled score on the standard test set of the RST Discourse Treebank corpus, which improves 5.0% compared to HILDA [6], a state-of-the-art discourse parsing system. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Xuan Bach, N., Le Minh, N., & Shimazu, A. (2012). UDRST: A novel system for unlabeled discourse parsing in the RST framework. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7614 LNAI, pp. 250–261). https://doi.org/10.1007/978-3-642-33983-7_25
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